Each week, we select a recently published Open Access article to feature. This week’s article comes from Statistics in Medicine and considers point process models for sweat gland activation observed with noise.
The article’s abstract is given below, with the full article available to read here.
Point process models for sweat gland activation observed with noise. Statistics in Medicine. 2021; 1– 18. https://doi.org/10.1002/sim.8891, , , .
The aim of this article is to construct spatial models for the activation of sweat glands for healthy subjects and subjects suffering from peripheral neuropathy by using videos of sweating recorded from the subjects. The sweat patterns are regarded as realizations of spatial point processes and two point process models for the sweat gland activation and two methods for inference are proposed. Several image analysis steps are needed to extract the point patterns from the videos and some incorrectly identified sweat gland locations may be present in the data. To take into account the errors, we either include an error term in the point process model or use an estimation procedure that is robust with respect to the errors.